Three Essays on Behavioral and Experimental Economics

The focus of this dissertation is to understand how mental rules of thumb, cognitive biases, and individual differences can lead judgments and decisions to systematically deviate from the theoretical “optimal” choices. The first essay examines how a decision-maker’s subjective belief is determined by her risk preference in a coordination game. We conduct a laboratory experiment where the participants played a repeated, fixed-partner stag-hunt game. In the experiment, we elicited the participants’ subjective belief, risk aversion and cautiousness levels. Here, we confirm the findings from past studies that suggest that the traditional measure of risk aversion in economics cannot explain people’s behavior. Additionally, we find that the psychological concept of cautiousness plays a key role in determining the origin and the evolution of the decision-maker’s belief. Specifically, we find that cautiousness affects the way people form the mental representation of their partners. A decision-maker with a higher cautiousness level is less likely to believe that her partner will choose the risky option. When the stag-hunt game was played repeatedly, a high cautiousness level prevents the decision-maker from updating her belief effectively, and consequently impedes cooperation between the players. The second essay proposes and experimentally tests the hypothesis that cognitive dissonance associated with the context plays a key role in determining people’s decisions in economic experiments. We conduct a laboratory bribery game experiment where the cognitive dissonance levels are controlled using different treatments (familiar-context treatment, unfamiliar-context treatment, and context-free treatment). With the aid of an independent attitude survey, we find that people in the unfamiliar-context treatment and the context-free treatment experience the same cognitive dissonance level; meanwhile, we do not observe different behavior in the lab. We also find the familiar-context treatment triggers the most intensive cognitive dissonance level among all treatments where the subjects are much less likely to behave unethically. Our theory is able to unify the mixed results from past studies on the experimental context effects. In the third essay, using a unique data set from a sample of recent local college graduates in China, we investigate the effect of agreeableness on the respondents’ starting salary and perceived career satisfaction level. Results from our analyses indicates that agreeableness positively predict women’s starting salary. This effect is highly robust to change in model specifications. However, agreeableness does not impact the men’s starting salary. Our result here suggests that non-cognitive ability (such as personality traits) plays a vital role in determining labor market outcome. In addition, we find that agreeableness positively related with subjective job satisfaction level. But this result is not robust to changes in model specifications. When we add the respondents’ major as a control variable, the effect of agreeableness on job-satisfaction becomes negligible and not statistically significant. This result might suggest a self-sorted story when choosing major. Further examination is required to explore this possibility

Intelligent Operation System for the Autonomous Vehicle Fleet

Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration (ADR). The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration, which is proven as beneficial in promoting fleet adaptability and in saving ownership costs. The objective of military fleet operation is to satisfy uncertain demands on time while providing vehicle maintenance. To quantify the benefits and burdens from modularity in military operation, a decision support system is required to yield autonomously operation strategies for comparing the (near) optimal fleet performance for different vehicle architectures under diverse scenarios. The problem is challenging because: 1) fleet operation strategies are numerous, especially when modularity is considered; 2) operation actions are time-delayed and time-varying; 3) vehicle damages and demands are highly uncertain; 4) available capacity for ADR actions and vehicle repair is constrained. Finally, to explore advanced tactics enabled by fleet modularity, the competition between human-like and adversarial forces is required, where each force is capable to autonomously perceive and analyze field information, learn enemy's behavior, forecast enemy's actions, and prepare an operation plan accordingly. Currently, methodologies developed specifically for fleet competition are only valid for single type of resources and simple operation rules, which are impossible to implement in modular fleet operation. This dissertation focuses on a new general methodology to yield decisions in operating a fleet of autonomous military vehicles/robots in both conventional and modular architectures. First, a stochastic state space model is created to represent the changes in fleet dynamics caused by operation actions. Then, a stochastic model predictive control is customized to manage the system dynamics, which is capable of real-time decision making. Including modularity increases the complexity of fleet operation problem, a novel intelligent agent based model is proposed to ensure the computational efficiency and also imitate the collaborative decisions making process of human-like commanders. Operation decisions are distributed to several agents with distinct responsibility. Agents are designed in a specific way to collaboratively make and adjust decisions through selectively sharing information, reasoning the causality between events, and learning the other's behavior, which are achieved by real-time optimization and artificial intelligence techniques. To evaluate the impacts from fleet modularity, three operation problems are formulated: (i) simplified logistic mission scenario: operate a fleet to guarantee the readiness of vehicles at battlefields considering the stochasticity in inventory stocks and mission requirements; (ii) tactical mission scenario: deliver resources to battlefields with stochastic requirements of vehicle repairs and maintenance; (iii) attacker-defender game: satisfy the mission requirements with minimized losses caused by uncertain assaults from an enemy. The model is also implemented for a civilian application, namely the real-time management of reconfigurable manufacturing systems (RMSs). As the number of RMS configurations increases exponentially with the size of the line and demand changes frequently, two challenges emerge: how to efficiently select the optimal configuration given limited resources, and how to allocate resources among lines. According to the ideas in modular fleet operation, a new mathematical approach is presented for distributing the stochastic demands and exchanging machines or modules among lines (which are groups of machines) as a bidding process, and for adaptively configuring these lines and machines for the resulting shared demand under a limited inventory of configurable components.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/1/lixingyu_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/2/lixingyu_1.pd

Coevolution of risk aversion, trust and trustworthiness: an agent-based approach

The research presented here deals with the evolution of personality features of humans engaged in strategic interactions. The evolution of risk aversion and trustworthiness is modelled and simulated in the context of a binary trust game, seeking the origin and end-points of an evolutionary process, accounting for different degrees of locality. This research has employed computer simulations in order to get dynamic equilibria in populations of players that keep evolving. The locality or global nature of interaction plays an important role. Risk aversion evolves together with trust and trustworthiness. Trust behaviour follows reciprocation attributes. Results of the simulations are equal to the ones elicited in empirical studies

Factors affecting the decision making of news editors in South Africa

The aim of this exploratory study is to gain an understanding of the factors which influence the decision making of news editors in South Africa. The independent news media is an important source of information in modern society. It has a significant influence on people’s perceptions of the political and social issues facing a society. However it is not a neutral institution as it is a commercial business driven by profit. Within news organisations, editors are key decision makers as they decide how resources are allocated and which stories enter the public domain. The decisions taken by editors are immediately open to public scrutiny and often impact a range of stakeholders in society. In this study an exploratory phenomenological approach was used, as this approach seeks to capture the meaning of an experience through an examination of an individual’s lived experiences. To achieve this, twelve, in-depth interviews were conducted with editors, with over 85 years of editorial experience, in order to establish which factors influence their decision making process. The data was analysed using content and frequency analysis. The main factors which the editors identified as influencing their decision making process when evaluating a story included the following: the relevance to the audience, accuracy, the public interest, newsworthiness and entertainment value. In difficult editorial decisions which involved a trade-off between two or more important factors, the editors showed a strong commitment to the journalistic values of acting in the public interest and newsworthiness. Consultation, knowledge and personal attributes emerged as important competencies in ensuring good editorial decisions. CopyrightDissertation (MBA)--University of Pretoria, 2010.Gordon Institute of Business Science (GIBS)unrestricte

行動ルールの進化・適応を考慮した社会システムのモデル化と分析

学位の種別: 論文博士審査委員会委員 : （主査）東京大学教授 大橋 弘, 東京大学准教授 白山 晋, 東京大学講師 藤井 秀, 東京大学教授 古田 一, 東京大学准教授 陳 昱University of Tokyo(東京大学

RISK PREFERENCE AND SEQUENTIAL CHOICE IN EVOLUTIONARY GAMES

There is much empirical evidence that human decision-making under risk does not coincide with expected value maximization, and much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking, sequentiality of choice, and population dynamics in evolutionary environments. In this paper, we provide the results of several evolutionary game simulations designed to study the risk behavior of agents in evolutionary environments. These include several evolutionary lottery games where sequential decisions are made between risky and safe choices, and an evolutionary version of the well-known stag hunt game. Our results show how agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes, and how this can facilitate the evolution of cooperation in situations where cooperation entails risk.Evolutionary games, decision theory, population dynamics, risk